Increasing advances in computer technology (e.g., microprocessor speed, memory capacity, data transfer bandwidth, software functionality, and the like) have generally contributed to increased computer application in various industries. Ever more powerful server systems, which are often configured as an array of servers, are often provided to service requests originating from external sources such as the World Wide Web, for example. As local Intranet systems have become more sophisticated thereby requiring servicing of larger network loads and related applications, internal system demands have grown accordingly as well. As such, much business data is stored in databases, under the management of a database management system (DBMS). For such DBMS systems, a demand for database transaction processing capacity in large installations has been growing significantly.
Thus, computers have become a necessary tool for many applications throughout the world. Typewriters and slide rules have become obsolete in light of keyboards coupled with sophisticated word-processing applications and calculators that include advanced mathematical functions/capabilities. Trending applications, analysis applications, and other applications that previously may have required a collection of mathematicians or other high-priced specialists to painstakingly complete by hand can now be accomplished through use of computer technology. For instance, due to ever-increasing processor and memory capabilities, if data is entered properly into an application/wizard, such application/wizard can automatically output a response nearly instantaneously (in comparison to hours or days generating such response by hand previously required).
Furthermore, through utilization of computers, vast magnitudes of data can be obtained for analysis and predictive purposes. For example, a retail sales establishment can employ a data analysis application to track sales of a particular good given a particular type of customer, income level of customers, a time of year, advertising strategy, and the like. More particularly, patterns within collected data can be determined and analyzed, and predictions relating to future events can be generated based upon these patterns.
Analysis of data, recognition of patterns, and generation of predictions based at least in part upon the recognized patterns can be collectively referred to as data mining. To enable data mining, various models can be programmed and trained. For instance, data previously collected can be employed as training data for one or more data mining models. The data mining models can employ various predictive analysis algorithms and can further utilize suitable clustering algorithms to cluster data analyzed by the data mining models.